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Polynomial Spaces: A New Framework for CompositetoPrimeOrder Transformations∗
"... At Eurocrypt 2010, Freeman presented a framework to convert cryptosystems based on compositeorder groups into ones that use primeorder groups. Such a transformation is interesting not only from a conceptual point of view, but also since for relevant parameters, operations in primeorder groups are ..."
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Cited by 3 (0 self)
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are faster than compositeorder operations by an order of magnitude. Since Freeman’s work, several other works have shown improvements, but also lower bounds on the efficiency of such conversions. In this work, we present a new framework for compositetoprimeorder conversions. Our framework
A NEW POLYNOMIALTIME ALGORITHM FOR LINEAR PROGRAMMING
 COMBINATORICA
, 1984
"... We present a new polynomialtime algorithm for linear programming. In the worst case, the algorithm requires O(tf'SL) arithmetic operations on O(L) bit numbers, where n is the number of variables and L is the number of bits in the input. The running,time of this algorithm is better than the ell ..."
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Cited by 860 (3 self)
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We present a new polynomialtime algorithm for linear programming. In the worst case, the algorithm requires O(tf'SL) arithmetic operations on O(L) bit numbers, where n is the number of variables and L is the number of bits in the input. The running,time of this algorithm is better than
PEGASUS: A policy search method for large MDPs and POMDPs
 In Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence
, 2000
"... We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a model. Our approach is based on the following observation: Any (PO)MDP can be transformed into an "equivalent&qu ..."
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Cited by 257 (9 self)
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We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a model. Our approach is based on the following observation: Any (PO)MDP can be transformed into an "
On problems without polynomial kernels
 LECT. NOTES COMPUT. SCI
, 2007
"... Kernelization is a strong and widelyapplied technique in parameterized complexity. In a nutshell, a kernelization algorithm, or simply a kernel, is a polynomialtime transformation that transforms any given parameterized instance to an equivalent instance of the same problem, with size and parame ..."
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Cited by 143 (17 self)
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Kernelization is a strong and widelyapplied technique in parameterized complexity. In a nutshell, a kernelization algorithm, or simply a kernel, is a polynomialtime transformation that transforms any given parameterized instance to an equivalent instance of the same problem, with size
Data and Computation Transformations for Multiprocessors
 In Proceedings of the Fifth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
, 1995
"... Effective memory hierarchy utilization is critical to the performance of modern multiprocessor architectures. We havedeveloped the first compiler system that fully automatically parallelizes sequential programs and changes the original array layouts to improve memory system performance. Our optimiza ..."
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Cited by 177 (15 self)
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optimization algorithm consists of two steps. The first step chooses the parallelization and computation assignment such that synchronization and data sharing are minimized. The second step then restructures the layout of the data in the shared address space with an algorithm that is based on a new data
Recognition of Shapes by Editing Their Shock Graphs
, 2004
"... This paper presents a novel framework for the recognition of objects based on their silhouettes. The main idea is to measure the distance between two shapes as the minimum extent of deformation necessary for one shape to match the other. Since the space of deformations is very highdimensional, thr ..."
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Cited by 204 (8 self)
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This paper presents a novel framework for the recognition of objects based on their silhouettes. The main idea is to measure the distance between two shapes as the minimum extent of deformation necessary for one shape to match the other. Since the space of deformations is very high
Universal SpaceTime Coding
 IEEE Trans. Inform. Theory
, 2003
"... A universal framework is developed for constructing fullrate and fulldiversity coherent spacetime codes for systems with arbitrary numbers of transmit and receive antennas. The proposed framework combines spacetime layering concepts with algebraic component codes optimized for singleinputsi ..."
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Cited by 143 (7 self)
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A universal framework is developed for constructing fullrate and fulldiversity coherent spacetime codes for systems with arbitrary numbers of transmit and receive antennas. The proposed framework combines spacetime layering concepts with algebraic component codes optimized for single
Learning attractor landscapes for learning motor primitives
 in Advances in Neural Information Processing Systems
, 2003
"... Many control problems take place in continuous stateaction spaces, e.g., as in manipulator robotics, where the control objective is often defined as finding a desired trajectory that reaches a particular goal state. While reinforcement learning offers a theoretical framework to learn such control p ..."
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Cited by 195 (28 self)
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Many control problems take place in continuous stateaction spaces, e.g., as in manipulator robotics, where the control objective is often defined as finding a desired trajectory that reaches a particular goal state. While reinforcement learning offers a theoretical framework to learn such control
Stochastic Partial Differential Equations driven by . . .
, 2004
"... In this paper we develop a white noise framework for the study of stochastic partial differential equations driven by a dparameter (pure jump) Lévy white noise. As an example we use this theory to solve the stochastic Poisson equation with respect to Lévy white noise for any dimension d. The soluti ..."
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Cited by 183 (8 self)
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polynomials. Based on this expansion we define Kondratiev spaces and the Lévy Hermite transform.
Temporal sequence learning and data reduction for anomaly detection
 ACM TRANSACTIONS ON INFORMATION SYSTEMS SECURITY
, 1999
"... The anomaly detection problem can be formulated as one of learning to characterize the behaviors of an individual, system, or network in terms of temporal sequences of discrete data. We present an approach to this problem based on instance based learning (IBL) techniques. To cast the anomaly detecti ..."
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Cited by 191 (6 self)
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detection task in an IBL framework, we employ an approach that transforms temporal sequences of discrete, unordered observations into a metric space via a similarity measure that encodes intraattribute dependencies. Classification boundaries are selected from an a posteriori characterization of the valid
Results 1  10
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